Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations80000
Missing cells166555
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 MiB
Average record size in memory200.0 B

Variable types

Text1
DateTime3
Categorical2
Numeric19

Alerts

Currency has constant value "USD" Constant
Fiscal Period has constant value "FY" Constant
Cost of Revenue is highly overall correlated with Depreciation & Amortization and 15 other fieldsHigh correlation
Depreciation & Amortization is highly overall correlated with Cost of Revenue and 10 other fieldsHigh correlation
Gross Profit is highly overall correlated with Cost of Revenue and 15 other fieldsHigh correlation
Income (Loss) from Continuing Operations is highly overall correlated with Cost of Revenue and 8 other fieldsHigh correlation
Income Tax (Expense) Benefit, Net is highly overall correlated with Cost of Revenue and 9 other fieldsHigh correlation
Interest Expense, Net is highly overall correlated with Cost of Revenue and 7 other fieldsHigh correlation
Net Income is highly overall correlated with Cost of Revenue and 8 other fieldsHigh correlation
Net Income (Common) is highly overall correlated with Cost of Revenue and 8 other fieldsHigh correlation
Non-Operating Income (Loss) is highly overall correlated with Depreciation & Amortization and 2 other fieldsHigh correlation
Operating Expenses is highly overall correlated with Cost of Revenue and 9 other fieldsHigh correlation
Operating Income (Loss) is highly overall correlated with Cost of Revenue and 11 other fieldsHigh correlation
Pretax Income (Loss) is highly overall correlated with Cost of Revenue and 8 other fieldsHigh correlation
Pretax Income (Loss), Adj. is highly overall correlated with Cost of Revenue and 8 other fieldsHigh correlation
Research & Development is highly overall correlated with Cost of Revenue and 7 other fieldsHigh correlation
Revenue is highly overall correlated with Cost of Revenue and 16 other fieldsHigh correlation
Selling, General & Administrative is highly overall correlated with Cost of Revenue and 9 other fieldsHigh correlation
Shares (Basic) is highly overall correlated with Cost of Revenue and 7 other fieldsHigh correlation
Shares (Diluted) is highly overall correlated with Cost of Revenue and 7 other fieldsHigh correlation
Shares (Diluted) has 1309 (1.6%) missing values Missing
Revenue has 8251 (10.3%) missing values Missing
Cost of Revenue has 17252 (21.6%) missing values Missing
Gross Profit has 17237 (21.5%) missing values Missing
Selling, General & Administrative has 4348 (5.4%) missing values Missing
Research & Development has 42557 (53.2%) missing values Missing
Depreciation & Amortization has 47663 (59.6%) missing values Missing
Non-Operating Income (Loss) has 1166 (1.5%) missing values Missing
Interest Expense, Net has 10005 (12.5%) missing values Missing
Income Tax (Expense) Benefit, Net has 15834 (19.8%) missing values Missing
Shares (Basic) is highly skewed (γ1 = 75.37405882) Skewed
Shares (Diluted) is highly skewed (γ1 = 81.24753918) Skewed
Revenue is highly skewed (γ1 = 100.6631293) Skewed
Cost of Revenue is highly skewed (γ1 = -104.9899306) Skewed
Gross Profit is highly skewed (γ1 = 76.60550478) Skewed
Operating Expenses is highly skewed (γ1 = -83.18410982) Skewed
Selling, General & Administrative is highly skewed (γ1 = -130.1201707) Skewed
Research & Development is highly skewed (γ1 = -86.43377693) Skewed
Depreciation & Amortization is highly skewed (γ1 = -69.26291487) Skewed
Operating Income (Loss) is highly skewed (γ1 = 55.58379665) Skewed
Non-Operating Income (Loss) is highly skewed (γ1 = -71.58231169) Skewed
Interest Expense, Net is highly skewed (γ1 = -110.6886051) Skewed
Pretax Income (Loss), Adj. is highly skewed (γ1 = 46.29920233) Skewed
Net Income (Common) is highly skewed (γ1 = 52.5506499) Skewed
Pretax Income (Loss) is highly skewed (γ1 = 35.03868242) Skewed
Income Tax (Expense) Benefit, Net is highly skewed (γ1 = 41.41969615) Skewed
Income (Loss) from Continuing Operations is highly skewed (γ1 = 60.34053871) Skewed
Net Income is highly skewed (γ1 = 50.78312041) Skewed

Reproduction

Analysis started2025-03-23 05:25:04.995246
Analysis finished2025-03-23 05:26:10.568258
Duration1 minute and 5.57 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Ticker
Text

Distinct4394
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
2025-03-23T05:26:10.937551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length4
Mean length3.6472625
Min length1

Characters and Unicode

Total characters291781
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
a 25
 
< 0.1%
aa 25
 
< 0.1%
aal 25
 
< 0.1%
aamc 25
 
< 0.1%
aan 25
 
< 0.1%
lqmt 25
 
< 0.1%
lrcx 25
 
< 0.1%
lrdc 25
 
< 0.1%
abus 25
 
< 0.1%
ac 25
 
< 0.1%
Other values (4384) 79750
99.7%
2025-03-23T05:26:11.409608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 22111
 
7.6%
C 20830
 
7.1%
R 19649
 
6.7%
T 19265
 
6.6%
S 18665
 
6.4%
N 16392
 
5.6%
L 15048
 
5.2%
I 14866
 
5.1%
E 14592
 
5.0%
P 13213
 
4.5%
Other values (32) 117150
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 22111
 
7.6%
C 20830
 
7.1%
R 19649
 
6.7%
T 19265
 
6.6%
S 18665
 
6.4%
N 16392
 
5.6%
L 15048
 
5.2%
I 14866
 
5.1%
E 14592
 
5.0%
P 13213
 
4.5%
Other values (32) 117150
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 22111
 
7.6%
C 20830
 
7.1%
R 19649
 
6.7%
T 19265
 
6.6%
S 18665
 
6.4%
N 16392
 
5.6%
L 15048
 
5.2%
I 14866
 
5.1%
E 14592
 
5.0%
P 13213
 
4.5%
Other values (32) 117150
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 22111
 
7.6%
C 20830
 
7.1%
R 19649
 
6.7%
T 19265
 
6.6%
S 18665
 
6.4%
N 16392
 
5.6%
L 15048
 
5.2%
I 14866
 
5.1%
E 14592
 
5.0%
P 13213
 
4.5%
Other values (32) 117150
40.1%
Distinct67
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
Minimum2019-01-10 00:00:00
Maximum2024-02-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-23T05:26:11.559851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:11.720043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Currency
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
USD
80000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters240000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 80000
100.0%

Length

2025-03-23T05:26:11.849957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T05:26:11.924302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usd 80000
100.0%

Most occurring characters

ValueCountFrequency (%)
U 80000
33.3%
S 80000
33.3%
D 80000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 240000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 80000
33.3%
S 80000
33.3%
D 80000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 240000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 80000
33.3%
S 80000
33.3%
D 80000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 240000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 80000
33.3%
S 80000
33.3%
D 80000
33.3%

Fiscal Year
Real number (ℝ)

Distinct79978
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.9729
Minimum1918.0604
Maximum2124.1322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size625.1 KiB
2025-03-23T05:26:12.012833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1918.0604
5-th percentile1930.0313
Q11970.4945
median2020.885
Q32071.7299
95-th percentile2111.9427
Maximum2124.1322
Range206.07178
Interquartile range (IQR)101.23536

Descriptive statistics

Standard deviation58.369402
Coefficient of variation (CV)0.028881832
Kurtosis-1.1978522
Mean2020.9729
Median Absolute Deviation (MAD)50.580752
Skewness0.00038191545
Sum1.6167784 × 108
Variance3406.987
MonotonicityNot monotonic
2025-03-23T05:26:12.163853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1964.759563 2
 
< 0.1%
2022.791145 2
 
< 0.1%
2093.017502 2
 
< 0.1%
2082.374871 2
 
< 0.1%
2059.977146 2
 
< 0.1%
1981.135959 2
 
< 0.1%
1952.073514 2
 
< 0.1%
1948.063041 2
 
< 0.1%
1948.345604 2
 
< 0.1%
1931.405812 2
 
< 0.1%
Other values (79968) 79980
> 99.9%
ValueCountFrequency (%)
1918.060378 1
< 0.1%
1918.069846 1
< 0.1%
1918.087215 1
< 0.1%
1918.113246 1
< 0.1%
1918.133544 1
< 0.1%
1918.150178 1
< 0.1%
1918.170088 1
< 0.1%
1918.170379 1
< 0.1%
1918.210091 1
< 0.1%
1918.220202 1
< 0.1%
ValueCountFrequency (%)
2124.132153 1
< 0.1%
2124.126801 1
< 0.1%
2124.11536 1
< 0.1%
2124.111386 1
< 0.1%
2124.10504 1
< 0.1%
2124.08847 1
< 0.1%
2124.055415 1
< 0.1%
2124.049607 1
< 0.1%
2124.028356 1
< 0.1%
2124.016445 1
< 0.1%

Fiscal Period
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
FY
80000 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters160000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFY
2nd rowFY
3rd rowFY
4th rowFY
5th rowFY

Common Values

ValueCountFrequency (%)
FY 80000
100.0%

Length

2025-03-23T05:26:12.270492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T05:26:12.339595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fy 80000
100.0%

Most occurring characters

ValueCountFrequency (%)
F 80000
50.0%
Y 80000
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 80000
50.0%
Y 80000
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 80000
50.0%
Y 80000
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 80000
50.0%
Y 80000
50.0%
Distinct1217
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
Minimum2007-02-04 00:00:00
Maximum2024-12-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-23T05:26:12.419422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:12.570004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1040
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size625.1 KiB
Minimum2007-02-04 00:00:00
Maximum2025-12-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-23T05:26:12.703315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:12.847899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Shares (Basic)
Real number (ℝ)

High correlation  Skewed 

Distinct79319
Distinct (%)100.0%
Missing681
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean3.2720377 × 108
Minimum0.95505491
Maximum7.09913 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size625.1 KiB
2025-03-23T05:26:12.979891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.95505491
5-th percentile1693045.6
Q119296837
median49881515
Q31.3194201 × 108
95-th percentile6.1954184 × 108
Maximum7.09913 × 1011
Range7.09913 × 1011
Interquartile range (IQR)1.1264518 × 108

Descriptive statistics

Standard deviation6.9717017 × 109
Coefficient of variation (CV)21.306911
Kurtosis6682.7941
Mean3.2720377 × 108
Median Absolute Deviation (MAD)39770100
Skewness75.374059
Sum2.5953476 × 1013
Variance4.8604624 × 1019
MonotonicityNot monotonic
2025-03-23T05:26:13.120689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667380599.3 1
 
< 0.1%
71417744.81 1
 
< 0.1%
61696069.69 1
 
< 0.1%
51992482.95 1
 
< 0.1%
50720845.16 1
 
< 0.1%
52712371.94 1
 
< 0.1%
50604437.65 1
 
< 0.1%
18144.81669 1
 
< 0.1%
14561918.01 1
 
< 0.1%
11451930.06 1
 
< 0.1%
Other values (79309) 79309
99.1%
(Missing) 681
 
0.9%
ValueCountFrequency (%)
0.955054915 1
< 0.1%
0.955239671 1
< 0.1%
0.962386277 1
< 0.1%
0.983338133 1
< 0.1%
1.010177348 1
< 0.1%
1.020273556 1
< 0.1%
1.021588296 1
< 0.1%
1.044449934 1
< 0.1%
2.851856867 1
< 0.1%
2.875178352 1
< 0.1%
ValueCountFrequency (%)
7.09913 × 10111
< 0.1%
7.06787 × 10111
< 0.1%
7.04681 × 10111
< 0.1%
6.71961 × 10111
< 0.1%
6.52084 × 10111
< 0.1%
3.54177 × 10111
< 0.1%
3.50564 × 10111
< 0.1%
3.46287 × 10111
< 0.1%
3.38914 × 10111
< 0.1%
3.34166 × 10111
< 0.1%

Shares (Diluted)
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct78690
Distinct (%)> 99.9%
Missing1309
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1.1857804 × 1011
Minimum0.95681254
Maximum8.33304 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size625.1 KiB
2025-03-23T05:26:13.345788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.95681254
5-th percentile1825481
Q120015139
median51381491
Q31.363625 × 108
95-th percentile6.5066503 × 108
Maximum8.33304 × 1014
Range8.33304 × 1014
Interquartile range (IQR)1.1634736 × 108

Descriptive statistics

Standard deviation9.4918234 × 1012
Coefficient of variation (CV)80.047062
Kurtosis6617.5021
Mean1.1857804 × 1011
Median Absolute Deviation (MAD)40931760
Skewness81.247539
Sum9.3310243 × 1015
Variance9.0094712 × 1025
MonotonicityNot monotonic
2025-03-23T05:26:13.555682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102329683.8 2
 
< 0.1%
52530115.16 1
 
< 0.1%
13898362.27 1
 
< 0.1%
12172989.05 1
 
< 0.1%
8689400.48 1
 
< 0.1%
6695759.659 1
 
< 0.1%
1088.847146 1
 
< 0.1%
1034525.728 1
 
< 0.1%
1104397.226 1
 
< 0.1%
1106501.036 1
 
< 0.1%
Other values (78680) 78680
98.4%
(Missing) 1309
 
1.6%
ValueCountFrequency (%)
0.956812538 1
< 0.1%
0.96090177 1
< 0.1%
0.97169622 1
< 0.1%
0.981557437 1
< 0.1%
0.982298792 1
< 0.1%
0.998142567 1
< 0.1%
0.999202023 1
< 0.1%
1.015110267 1
< 0.1%
1.018228129 1
< 0.1%
1.032137802 1
< 0.1%
ValueCountFrequency (%)
8.33304 × 10141
< 0.1%
8.20581 × 10141
< 0.1%
8.12069 × 10141
< 0.1%
7.96324 × 10141
< 0.1%
7.8056 × 10141
< 0.1%
7.72592 × 10141
< 0.1%
7.47047 × 10141
< 0.1%
7.3794 × 10141
< 0.1%
7.3223 × 10141
< 0.1%
7.3153 × 10141
< 0.1%

Revenue
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct71670
Distinct (%)99.9%
Missing8251
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean6.9993273 × 109
Minimum-1.95176 × 1011
Maximum1.28136 × 1013
Zeros78
Zeros (%)0.1%
Negative102
Negative (%)0.1%
Memory size625.1 KiB
2025-03-23T05:26:14.533287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.95176 × 1011
5-th percentile2135606.7
Q11.080633 × 108
median6.5279325 × 108
Q32.9413382 × 109
95-th percentile2.0662553 × 1010
Maximum1.28136 × 1013
Range1.3008776 × 1013
Interquartile range (IQR)2.8332749 × 109

Descriptive statistics

Standard deviation1.0612183 × 1011
Coefficient of variation (CV)15.161719
Kurtosis11704.467
Mean6.9993273 × 109
Median Absolute Deviation (MAD)6.3672211 × 108
Skewness100.66313
Sum5.0219474 × 1014
Variance1.1261843 × 1022
MonotonicityNot monotonic
2025-03-23T05:26:14.773201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
0.1%
1.21858 × 10112
 
< 0.1%
1.58222 × 10112
 
< 0.1%
48949405.05 1
 
< 0.1%
139924.3991 1
 
< 0.1%
35121.8164 1
 
< 0.1%
144856.3895 1
 
< 0.1%
347807.3489 1
 
< 0.1%
1701242.934 1
 
< 0.1%
1.477970525 × 10101
 
< 0.1%
Other values (71660) 71660
89.6%
(Missing) 8251
 
10.3%
ValueCountFrequency (%)
-1.95176 × 10111
< 0.1%
-1.95071 × 10111
< 0.1%
-1.88471 × 10111
< 0.1%
-1.81376 × 10111
< 0.1%
-1.67221 × 10111
< 0.1%
-1.63192 × 10111
< 0.1%
-1.58599 × 10111
< 0.1%
-1.56301 × 10111
< 0.1%
-1.42709 × 10111
< 0.1%
-1.42216 × 10111
< 0.1%
ValueCountFrequency (%)
1.28136 × 10131
< 0.1%
1.27947 × 10131
< 0.1%
1.27366 × 10131
< 0.1%
1.26464 × 10131
< 0.1%
3.7056 × 10121
< 0.1%
3.54266 × 10121
< 0.1%
3.42427 × 10121
< 0.1%
3.41249 × 10121
< 0.1%
3.37186 × 10121
< 0.1%
3.19356 × 10121
< 0.1%

Cost of Revenue
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct62734
Distinct (%)> 99.9%
Missing17252
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean-4.8106864 × 109
Minimum-7.91742 × 1012
Maximum3.1344781 × 109
Zeros14
Zeros (%)< 0.1%
Negative62658
Negative (%)78.3%
Memory size625.1 KiB
2025-03-23T05:26:14.987947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.91742 × 1012
5-th percentile-1.4948599 × 1010
Q1-1.8860279 × 109
median-3.8792319 × 108
Q3-60933755
95-th percentile-2684940.4
Maximum3.1344781 × 109
Range7.9205545 × 1012
Interquartile range (IQR)1.8250942 × 109

Descriptive statistics

Standard deviation6.5011185 × 1010
Coefficient of variation (CV)-13.51391
Kurtosis12328.241
Mean-4.8106864 × 109
Median Absolute Deviation (MAD)3.7796281 × 108
Skewness-104.98993
Sum-3.0186095 × 1014
Variance4.2264541 × 1021
MonotonicityNot monotonic
2025-03-23T05:26:15.224812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
< 0.1%
-1.0951 × 10112
 
< 0.1%
-178182738.7 1
 
< 0.1%
-51761589.38 1
 
< 0.1%
-31737770.68 1
 
< 0.1%
-31901972.21 1
 
< 0.1%
-15701682.31 1
 
< 0.1%
-9469.800589 1
 
< 0.1%
-585963935.9 1
 
< 0.1%
-511824228.3 1
 
< 0.1%
Other values (62724) 62724
78.4%
(Missing) 17252
 
21.6%
ValueCountFrequency (%)
-7.91742 × 10121
< 0.1%
-7.82252 × 10121
< 0.1%
-7.69138 × 10121
< 0.1%
-7.19125 × 10121
< 0.1%
-8.61188 × 10111
< 0.1%
-8.58461 × 10111
< 0.1%
-8.48312 × 10111
< 0.1%
-8.35486 × 10111
< 0.1%
-8.35432 × 10111
< 0.1%
-8.21381 × 10111
< 0.1%
ValueCountFrequency (%)
3134478067 1
< 0.1%
3072040597 1
< 0.1%
3008553329 1
< 0.1%
2967249241 1
< 0.1%
2931889522 1
< 0.1%
435694629.5 1
< 0.1%
425018388.2 1
< 0.1%
417109011 1
< 0.1%
414804696.9 1
< 0.1%
409874303.6 1
< 0.1%

Gross Profit
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct62740
Distinct (%)> 99.9%
Missing17237
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean3.0585736 × 109
Minimum-5.8739749 × 109
Maximum5.53863 × 1012
Zeros24
Zeros (%)< 0.1%
Negative2094
Negative (%)2.6%
Memory size625.1 KiB
2025-03-23T05:26:15.448522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5.8739749 × 109
5-th percentile504062.25
Q164455785
median3.2440655 × 108
Q31.2581991 × 109
95-th percentile8.2038518 × 109
Maximum5.53863 × 1012
Range5.544504 × 1012
Interquartile range (IQR)1.1937433 × 109

Descriptive statistics

Standard deviation5.390281 × 1010
Coefficient of variation (CV)17.623512
Kurtosis6769.113
Mean3.0585736 × 109
Median Absolute Deviation (MAD)3.1146909 × 108
Skewness76.605505
Sum1.9196526 × 1014
Variance2.9055129 × 1021
MonotonicityNot monotonic
2025-03-23T05:26:15.629662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
< 0.1%
-460953.4565 1
 
< 0.1%
-1803666.307 1
 
< 0.1%
-3318989.626 1
 
< 0.1%
-2814791.405 1
 
< 0.1%
89519076.9 1
 
< 0.1%
85161727.65 1
 
< 0.1%
71455020.99 1
 
< 0.1%
-6699.692863 1
 
< 0.1%
-7497969.614 1
 
< 0.1%
Other values (62730) 62730
78.4%
(Missing) 17237
 
21.5%
ValueCountFrequency (%)
-5873974919 1
< 0.1%
-5619851935 1
< 0.1%
-5574362684 1
< 0.1%
-5538320111 1
< 0.1%
-5522488024 1
< 0.1%
-5373108830 1
< 0.1%
-5358279596 1
< 0.1%
-5335321676 1
< 0.1%
-5285869388 1
< 0.1%
-5223437288 1
< 0.1%
ValueCountFrequency (%)
5.53863 × 10121
< 0.1%
5.3653 × 10121
< 0.1%
5.25989 × 10121
< 0.1%
5.13403 × 10121
< 0.1%
2.8452 × 10121
< 0.1%
2.67613 × 10121
< 0.1%
2.67092 × 10121
< 0.1%
2.66687 × 10121
< 0.1%
2.4512 × 10121
< 0.1%
2.39032 × 10121
< 0.1%

Operating Expenses
Real number (ℝ)

High correlation  Skewed 

Distinct79810
Distinct (%)100.0%
Missing190
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-1.899751 × 109
Minimum-4.78356 × 1012
Maximum9.695547 × 108
Zeros0
Zeros (%)0.0%
Negative79564
Negative (%)99.5%
Memory size625.1 KiB
2025-03-23T05:26:15.770489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.78356 × 1012
5-th percentile-4.7205736 × 109
Q1-6.9659051 × 108
median-1.9716258 × 108
Q3-50586536
95-th percentile-4701062
Maximum9.695547 × 108
Range4.7845296 × 1012
Interquartile range (IQR)6.4600397 × 108

Descriptive statistics

Standard deviation4.3251713 × 1010
Coefficient of variation (CV)-22.767043
Kurtosis7772.2328
Mean-1.899751 × 109
Median Absolute Deviation (MAD)1.7699934 × 108
Skewness-83.18411
Sum-1.5161913 × 1014
Variance1.8707107 × 1021
MonotonicityNot monotonic
2025-03-23T05:26:15.906102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-522282444.5 1
 
< 0.1%
-375119962.3 1
 
< 0.1%
-344540355.7 1
 
< 0.1%
-308296846.4 1
 
< 0.1%
-107441060.6 1
 
< 0.1%
-108682810.9 1
 
< 0.1%
-106135089.3 1
 
< 0.1%
-159854921.1 1
 
< 0.1%
-282085902.7 1
 
< 0.1%
-253866232.6 1
 
< 0.1%
Other values (79800) 79800
99.8%
(Missing) 190
 
0.2%
ValueCountFrequency (%)
-4.78356 × 10121
< 0.1%
-4.62939 × 10121
< 0.1%
-4.56524 × 10121
< 0.1%
-4.49846 × 10121
< 0.1%
-2.79945 × 10121
< 0.1%
-2.72285 × 10121
< 0.1%
-2.62823 × 10121
< 0.1%
-2.62521 × 10121
< 0.1%
-2.34134 × 10121
< 0.1%
-2.32906 × 10121
< 0.1%
ValueCountFrequency (%)
969554697.1 1
< 0.1%
947788600.5 1
< 0.1%
921575187 1
< 0.1%
904377106 1
< 0.1%
901938432.8 1
< 0.1%
170696296.2 1
< 0.1%
165321502.4 1
< 0.1%
165299124.2 1
< 0.1%
161674610.1 1
< 0.1%
159098717 1
< 0.1%

Selling, General & Administrative
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct75652
Distinct (%)100.0%
Missing4348
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean-8.6735499 × 108
Minimum-2.3465 × 1012
Maximum38644336
Zeros0
Zeros (%)0.0%
Negative75485
Negative (%)94.4%
Memory size625.1 KiB
2025-03-23T05:26:16.038552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.3465 × 1012
5-th percentile-2.8402725 × 109
Q1-3.996519 × 108
median-1.0028245 × 108
Q3-22692995
95-th percentile-3299280
Maximum38644336
Range2.3465386 × 1012
Interquartile range (IQR)3.7695891 × 108

Descriptive statistics

Standard deviation1.6833751 × 1010
Coefficient of variation (CV)-19.408144
Kurtosis17569.138
Mean-8.6735499 × 108
Median Absolute Deviation (MAD)91150097
Skewness-130.12017
Sum-6.5617139 × 1013
Variance2.8337516 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:16.187718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3254099824 1
 
< 0.1%
-2886181104 1
 
< 0.1%
-2663651782 1
 
< 0.1%
-2474109697 1
 
< 0.1%
-2284016523 1
 
< 0.1%
-49120494.43 1
 
< 0.1%
-5852973439 1
 
< 0.1%
-118643224.7 1
 
< 0.1%
-29314826.85 1
 
< 0.1%
-28417040.12 1
 
< 0.1%
Other values (75642) 75642
94.6%
(Missing) 4348
 
5.4%
ValueCountFrequency (%)
-2.3465 × 10121
< 0.1%
-2.31842 × 10121
< 0.1%
-2.21708 × 10121
< 0.1%
-2.20242 × 10121
< 0.1%
-1.36547 × 10111
< 0.1%
-1.3379 × 10111
< 0.1%
-1.31893 × 10111
< 0.1%
-1.30599 × 10111
< 0.1%
-1.29963 × 10111
< 0.1%
-1.28688 × 10111
< 0.1%
ValueCountFrequency (%)
38644335.94 1
< 0.1%
38132799.61 1
< 0.1%
37124168.65 1
< 0.1%
36389318.49 1
< 0.1%
36358365.26 1
< 0.1%
34886429.92 1
< 0.1%
34554797.73 1
< 0.1%
34391454.96 1
< 0.1%
34262882.81 1
< 0.1%
33039318.34 1
< 0.1%

Research & Development
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct37443
Distinct (%)100.0%
Missing42557
Missing (%)53.2%
Infinite0
Infinite (%)0.0%
Mean-8.2682069 × 108
Minimum-3.85554 × 1012
Maximum1.2976855 × 108
Zeros0
Zeros (%)0.0%
Negative37338
Negative (%)46.7%
Memory size625.1 KiB
2025-03-23T05:26:16.314078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.85554 × 1012
5-th percentile-1.106396 × 109
Q1-1.2685259 × 108
median-43398795
Q3-11862720
95-th percentile-1147399.7
Maximum1.2976855 × 108
Range3.8556698 × 1012
Interquartile range (IQR)1.1498987 × 108

Descriptive statistics

Standard deviation4.3403365 × 1010
Coefficient of variation (CV)-52.49429
Kurtosis7486.5079
Mean-8.2682069 × 108
Median Absolute Deviation (MAD)37900386
Skewness-86.433777
Sum-3.0958647 × 1013
Variance1.8838521 × 1021
MonotonicityNot monotonic
2025-03-23T05:26:16.465089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-686171.6558 1
 
< 0.1%
-4151442.46 1
 
< 0.1%
-3287071.347 1
 
< 0.1%
-3731996.352 1
 
< 0.1%
-5602733.974 1
 
< 0.1%
-7394444.538 1
 
< 0.1%
-99805942.05 1
 
< 0.1%
-93212202.19 1
 
< 0.1%
-169321662.7 1
 
< 0.1%
-2127710.028 1
 
< 0.1%
Other values (37433) 37433
46.8%
(Missing) 42557
53.2%
ValueCountFrequency (%)
-3.85554 × 10121
< 0.1%
-3.82902 × 10121
< 0.1%
-3.7843 × 10121
< 0.1%
-3.74601 × 10121
< 0.1%
-3.54524 × 10121
< 0.1%
-4.522747576 × 10101
< 0.1%
-4.461190501 × 10101
< 0.1%
-4.434270995 × 10101
< 0.1%
-4.360490567 × 10101
< 0.1%
-4.326005042 × 10101
< 0.1%
ValueCountFrequency (%)
129768551.1 1
< 0.1%
128310725.5 1
< 0.1%
127513041.2 1
< 0.1%
127033744.9 1
< 0.1%
120842440.1 1
< 0.1%
120211930.2 1
< 0.1%
117001371.8 1
< 0.1%
115798099.7 1
< 0.1%
87555341.78 1
< 0.1%
85509232.23 1
< 0.1%

Depreciation & Amortization
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct32337
Distinct (%)100.0%
Missing47663
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean-1.1690657 × 109
Minimum-3.52151 × 1012
Maximum3073519.1
Zeros0
Zeros (%)0.0%
Negative32321
Negative (%)40.4%
Memory size625.1 KiB
2025-03-23T05:26:16.597742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.52151 × 1012
5-th percentile-1.5712544 × 109
Q1-2.2669654 × 108
median-52575604
Q3-10669243
95-th percentile-507017.17
Maximum3073519.1
Range3.5215131 × 1012
Interquartile range (IQR)2.160273 × 108

Descriptive statistics

Standard deviation4.2918652 × 1010
Coefficient of variation (CV)-36.711925
Kurtosis5224.3036
Mean-1.1690657 × 109
Median Absolute Deviation (MAD)50515485
Skewness-69.262915
Sum-3.7804077 × 1013
Variance1.8420107 × 1021
MonotonicityNot monotonic
2025-03-23T05:26:16.749817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-135914314.8 1
 
< 0.1%
-50029395.93 1
 
< 0.1%
-45362165.31 1
 
< 0.1%
-93543032.23 1
 
< 0.1%
-70788474.67 1
 
< 0.1%
-68931588.36 1
 
< 0.1%
-57820942.59 1
 
< 0.1%
-53576211.08 1
 
< 0.1%
-62932634.32 1
 
< 0.1%
-11491846.38 1
 
< 0.1%
Other values (32327) 32327
40.4%
(Missing) 47663
59.6%
ValueCountFrequency (%)
-3.52151 × 10121
< 0.1%
-3.45609 × 10121
< 0.1%
-3.37586 × 10121
< 0.1%
-3.34043 × 10121
< 0.1%
-1.56223 × 10121
< 0.1%
-1.53644 × 10121
< 0.1%
-1.49079 × 10121
< 0.1%
-1.48562 × 10121
< 0.1%
-8.60396 × 10111
< 0.1%
-8.58517 × 10111
< 0.1%
ValueCountFrequency (%)
3073519.071 1
< 0.1%
2926879.238 1
< 0.1%
2909927.712 1
< 0.1%
2879105.548 1
< 0.1%
899509.1058 1
< 0.1%
887759.1222 1
< 0.1%
882234.6583 1
< 0.1%
863395.7481 1
< 0.1%
5143.580896 1
< 0.1%
5002.275832 1
< 0.1%

Operating Income (Loss)
Real number (ℝ)

High correlation  Skewed 

Distinct79978
Distinct (%)100.0%
Missing22
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.2378113 × 108
Minimum-2.05918 × 1012
Maximum3.13855 × 1012
Zeros0
Zeros (%)0.0%
Negative34381
Negative (%)43.0%
Memory size625.1 KiB
2025-03-23T05:26:16.892587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.05918 × 1012
5-th percentile-1.8487313 × 108
Q1-23356647
median14456173
Q32.4838574 × 108
95-th percentile2.6208133 × 109
Maximum3.13855 × 1012
Range5.19773 × 1012
Interquartile range (IQR)2.7174239 × 108

Descriptive statistics

Standard deviation2.6348047 × 1010
Coefficient of variation (CV)42.239249
Kurtosis10750.544
Mean6.2378113 × 108
Median Absolute Deviation (MAD)81731026
Skewness55.583797
Sum4.9888767 × 1013
Variance6.9421957 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:17.031422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-384072722.4 1
 
< 0.1%
-349113562 1
 
< 0.1%
-318204142.1 1
 
< 0.1%
1236865.53 1
 
< 0.1%
8831837.189 1
 
< 0.1%
8553130.752 1
 
< 0.1%
-30406183.04 1
 
< 0.1%
-157164227.7 1
 
< 0.1%
1358949177 1
 
< 0.1%
38894212.48 1
 
< 0.1%
Other values (79968) 79968
> 99.9%
(Missing) 22
 
< 0.1%
ValueCountFrequency (%)
-2.05918 × 10121
< 0.1%
-2.04616 × 10121
< 0.1%
-2.02911 × 10121
< 0.1%
-1.90456 × 10121
< 0.1%
-2.77353 × 10111
< 0.1%
-2.70957 × 10111
< 0.1%
-2.62853 × 10111
< 0.1%
-2.602 × 10111
< 0.1%
-2.47359 × 10111
< 0.1%
-2.43237 × 10111
< 0.1%
ValueCountFrequency (%)
3.13855 × 10121
< 0.1%
3.1119 × 10121
< 0.1%
3.01862 × 10121
< 0.1%
2.94426 × 10121
< 0.1%
1.64704 × 10111
< 0.1%
1.62576 × 10111
< 0.1%
1.59256 × 10111
< 0.1%
1.56666 × 10111
< 0.1%
1.25128 × 10111
< 0.1%
1.20105 × 10111
< 0.1%

Non-Operating Income (Loss)
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct78805
Distinct (%)> 99.9%
Missing1166
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean-1.2332387 × 108
Minimum-1.03713 × 1012
Maximum4.35703 × 1011
Zeros30
Zeros (%)< 0.1%
Negative54075
Negative (%)67.6%
Memory size625.1 KiB
2025-03-23T05:26:17.159262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.03713 × 1012
5-th percentile-4.0188417 × 108
Q1-46686345
median-3971888.1
Q3264057.17
95-th percentile23908382
Maximum4.35703 × 1011
Range1.472833 × 1012
Interquartile range (IQR)46950402

Descriptive statistics

Standard deviation8.9465808 × 109
Coefficient of variation (CV)-72.545409
Kurtosis8679.5394
Mean-1.2332387 × 108
Median Absolute Deviation (MAD)9788174.9
Skewness-71.582312
Sum-9.7221142 × 1012
Variance8.0041309 × 1019
MonotonicityNot monotonic
2025-03-23T05:26:17.292423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
< 0.1%
15085895.96 1
 
< 0.1%
36670667.24 1
 
< 0.1%
20198415.74 1
 
< 0.1%
-10954617.76 1
 
< 0.1%
-5754498.221 1
 
< 0.1%
14903926.45 1
 
< 0.1%
6638165.771 1
 
< 0.1%
14490103.25 1
 
< 0.1%
-96212963.65 1
 
< 0.1%
Other values (78795) 78795
98.5%
(Missing) 1166
 
1.5%
ValueCountFrequency (%)
-1.03713 × 10121
< 0.1%
-1.02246 × 10121
< 0.1%
-9.92681 × 10111
< 0.1%
-9.69174 × 10111
< 0.1%
-3.87012 × 10111
< 0.1%
-3.84893 × 10111
< 0.1%
-3.63473 × 10111
< 0.1%
-3.51468 × 10111
< 0.1%
-3.28855 × 10111
< 0.1%
-3.22005 × 10111
< 0.1%
ValueCountFrequency (%)
4.35703 × 10111
< 0.1%
4.31529 × 10111
< 0.1%
4.2187 × 10111
< 0.1%
4.12173 × 10111
< 0.1%
2.81252 × 10111
< 0.1%
2.77864 × 10111
< 0.1%
2.75529 × 10111
< 0.1%
2.67278 × 10111
< 0.1%
9.873424186 × 10101
< 0.1%
9.803628026 × 10101
< 0.1%

Interest Expense, Net
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct69980
Distinct (%)> 99.9%
Missing10005
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean-1.9736759 × 108
Minimum-1.44104 × 1012
Maximum2.03218 × 1011
Zeros16
Zeros (%)< 0.1%
Negative55710
Negative (%)69.6%
Memory size625.1 KiB
2025-03-23T05:26:17.432916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.44104 × 1012
5-th percentile-4.7171541 × 108
Q1-66652525
median-9224226.9
Q3-156019.73
95-th percentile5106092
Maximum2.03218 × 1011
Range1.644258 × 1012
Interquartile range (IQR)66496505

Descriptive statistics

Standard deviation1.1255375 × 1010
Coefficient of variation (CV)-57.027473
Kurtosis13451.061
Mean-1.9736759 × 108
Median Absolute Deviation (MAD)10925778
Skewness-110.68861
Sum-1.3814744 × 1013
Variance1.2668346 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:17.579976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
< 0.1%
-311318011.4 1
 
< 0.1%
-303145663.4 1
 
< 0.1%
-295223811.3 1
 
< 0.1%
-76615168.64 1
 
< 0.1%
-658637732 1
 
< 0.1%
-691173665.9 1
 
< 0.1%
-6441935.942 1
 
< 0.1%
-305866465.1 1
 
< 0.1%
-6000509.917 1
 
< 0.1%
Other values (69970) 69970
87.5%
(Missing) 10005
 
12.5%
ValueCountFrequency (%)
-1.44104 × 10121
< 0.1%
-1.40447 × 10121
< 0.1%
-1.38816 × 10121
< 0.1%
-1.31864 × 10121
< 0.1%
-4.69814 × 10111
< 0.1%
-4.57095 × 10111
< 0.1%
-4.52354 × 10111
< 0.1%
-4.35121 × 10111
< 0.1%
-7.404349504 × 10101
< 0.1%
-7.179989546 × 10101
< 0.1%
ValueCountFrequency (%)
2.03218 × 10111
< 0.1%
1.98021 × 10111
< 0.1%
1.96227 × 10111
< 0.1%
1.95529 × 10111
< 0.1%
1.81838 × 10111
< 0.1%
1.80937 × 10111
< 0.1%
1.80584 × 10111
< 0.1%
1.78888 × 10111
< 0.1%
2405456425 1
< 0.1%
2382400329 1
< 0.1%

Pretax Income (Loss), Adj.
Real number (ℝ)

High correlation  Skewed 

Distinct79975
Distinct (%)> 99.9%
Missing22
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.1959374 × 108
Minimum-1.65082 × 1012
Maximum2.79058 × 1012
Zeros4
Zeros (%)< 0.1%
Negative37026
Negative (%)46.3%
Memory size625.1 KiB
2025-03-23T05:26:17.730041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.65082 × 1012
5-th percentile-2.2414412 × 108
Q1-32226590
median4491974.1
Q31.9263312 × 108
95-th percentile2.3343047 × 109
Maximum2.79058 × 1012
Range4.4414 × 1012
Interquartile range (IQR)2.2485971 × 108

Descriptive statistics

Standard deviation2.4505697 × 1010
Coefficient of variation (CV)47.163187
Kurtosis8655.8428
Mean5.1959374 × 108
Median Absolute Deviation (MAD)74145684
Skewness46.299202
Sum4.1556068 × 1013
Variance6.0052918 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:17.862943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
< 0.1%
-376762566.5 1
 
< 0.1%
-289481629.6 1
 
< 0.1%
-33633256.7 1
 
< 0.1%
2962771.566 1
 
< 0.1%
-17032548.38 1
 
< 0.1%
-70684076.08 1
 
< 0.1%
-265001551.8 1
 
< 0.1%
-233794695.3 1
 
< 0.1%
-23093209.91 1
 
< 0.1%
Other values (79965) 79965
> 99.9%
(Missing) 22
 
< 0.1%
ValueCountFrequency (%)
-1.65082 × 10121
< 0.1%
-1.58596 × 10121
< 0.1%
-1.55513 × 10121
< 0.1%
-1.51471 × 10121
< 0.1%
-1.34419 × 10121
< 0.1%
-1.3107 × 10121
< 0.1%
-1.2849 × 10121
< 0.1%
-1.24218 × 10121
< 0.1%
-2.40739 × 10111
< 0.1%
-2.37829 × 10111
< 0.1%
ValueCountFrequency (%)
2.79058 × 10121
< 0.1%
2.74006 × 10121
< 0.1%
2.64974 × 10121
< 0.1%
2.62224 × 10121
< 0.1%
2.7053 × 10111
< 0.1%
2.62046 × 10111
< 0.1%
2.58155 × 10111
< 0.1%
2.55617 × 10111
< 0.1%
2.16181 × 10111
< 0.1%
2.14529 × 10111
< 0.1%

Net Income (Common)
Real number (ℝ)

High correlation  Skewed 

Distinct79849
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9335449 × 108
Minimum-1.4533 × 1012
Maximum2.25892 × 1012
Zeros152
Zeros (%)0.2%
Negative39229
Negative (%)49.0%
Memory size625.1 KiB
2025-03-23T05:26:18.005880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4533 × 1012
5-th percentile-3.3307316 × 108
Q1-44112490
median570461.91
Q31.3484035 × 108
95-th percentile1.797879 × 109
Maximum2.25892 × 1012
Range3.71222 × 1012
Interquartile range (IQR)1.7895284 × 108

Descriptive statistics

Standard deviation1.8999212 × 1010
Coefficient of variation (CV)48.300485
Kurtosis10047.489
Mean3.9335449 × 108
Median Absolute Deviation (MAD)71504943
Skewness52.55065
Sum3.1468359 × 1013
Variance3.6097007 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:18.146850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 152
 
0.2%
-316085318.1 1
 
< 0.1%
-394953224.4 1
 
< 0.1%
-60352044.09 1
 
< 0.1%
-31430201.82 1
 
< 0.1%
-66407846.69 1
 
< 0.1%
-50940407.19 1
 
< 0.1%
-241316556.4 1
 
< 0.1%
-229134492.3 1
 
< 0.1%
933737955.1 1
 
< 0.1%
Other values (79839) 79839
99.8%
ValueCountFrequency (%)
-1.4533 × 10121
< 0.1%
-1.44042 × 10121
< 0.1%
-1.40704 × 10121
< 0.1%
-1.34341 × 10121
< 0.1%
-5.62434 × 10111
< 0.1%
-5.50072 × 10111
< 0.1%
-5.49397 × 10111
< 0.1%
-5.44181 × 10111
< 0.1%
-5.543984194 × 10101
< 0.1%
-5.419922903 × 10101
< 0.1%
ValueCountFrequency (%)
2.25892 × 10121
< 0.1%
2.17218 × 10121
< 0.1%
2.13183 × 10121
< 0.1%
2.10221 × 10121
< 0.1%
1.08984 × 10111
< 0.1%
1.08818 × 10111
< 0.1%
1.06334 × 10111
< 0.1%
1.06041 × 10111
< 0.1%
1.01746 × 10111
< 0.1%
1.01728 × 10111
< 0.1%

Pretax Income (Loss)
Real number (ℝ)

High correlation  Skewed 

Distinct79979
Distinct (%)> 99.9%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.2418117 × 108
Minimum-1.6175 × 1012
Maximum2.84219 × 1012
Zeros4
Zeros (%)< 0.1%
Negative38622
Negative (%)48.3%
Memory size625.1 KiB
2025-03-23T05:26:18.283948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.6175 × 1012
5-th percentile-3.3062016 × 108
Q1-43608502
median1616831.4
Q31.7160212 × 108
95-th percentile2.2128972 × 109
Maximum2.84219 × 1012
Range4.45969 × 1012
Interquartile range (IQR)2.1521062 × 108

Descriptive statistics

Standard deviation2.643003 × 1010
Coefficient of variation (CV)62.308353
Kurtosis7142.5636
Mean4.2418117 × 108
Median Absolute Deviation (MAD)78559985
Skewness35.038682
Sum3.3926859 × 1013
Variance6.985465 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:18.422277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
< 0.1%
-343293222.8 1
 
< 0.1%
-315188051.1 1
 
< 0.1%
-33846143.87 1
 
< 0.1%
-3607817.23 1
 
< 0.1%
-21016280.81 1
 
< 0.1%
-77191436.9 1
 
< 0.1%
-246055016.5 1
 
< 0.1%
-229721196.2 1
 
< 0.1%
-342823067.6 1
 
< 0.1%
Other values (79969) 79969
> 99.9%
(Missing) 18
 
< 0.1%
ValueCountFrequency (%)
-1.6175 × 10121
< 0.1%
-1.5425 × 10121
< 0.1%
-1.53597 × 10121
< 0.1%
-1.51666 × 10121
< 0.1%
-1.28679 × 10121
< 0.1%
-1.27692 × 10121
< 0.1%
-1.26392 × 10121
< 0.1%
-1.25596 × 10121
< 0.1%
-1.25388 × 10121
< 0.1%
-1.25102 × 10121
< 0.1%
ValueCountFrequency (%)
2.84219 × 10121
< 0.1%
2.80589 × 10121
< 0.1%
2.79963 × 10121
< 0.1%
2.58666 × 10121
< 0.1%
2.67038 × 10111
< 0.1%
2.66608 × 10111
< 0.1%
2.59591 × 10111
< 0.1%
2.59012 × 10111
< 0.1%
1.91114 × 10111
< 0.1%
1.84722 × 10111
< 0.1%

Income Tax (Expense) Benefit, Net
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct64086
Distinct (%)99.9%
Missing15834
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean-1.224606 × 108
Minimum-5.10153 × 1011
Maximum7.71572 × 1011
Zeros81
Zeros (%)0.1%
Negative49502
Negative (%)61.9%
Memory size625.1 KiB
2025-03-23T05:26:18.555559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5.10153 × 1011
5-th percentile-5.3643492 × 108
Q1-50558677
median-4132217.4
Q3-13449.674
95-th percentile35290158
Maximum7.71572 × 1011
Range1.281725 × 1012
Interquartile range (IQR)50545227

Descriptive statistics

Standard deviation7.5012357 × 109
Coefficient of variation (CV)-61.254277
Kurtosis7443.4236
Mean-1.224606 × 108
Median Absolute Deviation (MAD)9590356.2
Skewness41.419696
Sum-7.857807 × 1012
Variance5.6268537 × 1019
MonotonicityNot monotonic
2025-03-23T05:26:18.701986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
0.1%
-45910601.59 1
 
< 0.1%
-51664306.46 1
 
< 0.1%
-145174541.7 1
 
< 0.1%
-171538683.2 1
 
< 0.1%
-27851551.78 1
 
< 0.1%
-20361493.04 1
 
< 0.1%
-19332329.21 1
 
< 0.1%
8938252.109 1
 
< 0.1%
5647392.211 1
 
< 0.1%
Other values (64076) 64076
80.1%
(Missing) 15834
 
19.8%
ValueCountFrequency (%)
-5.10153 × 10111
< 0.1%
-5.09099 × 10111
< 0.1%
-5.05389 × 10111
< 0.1%
-5.04484 × 10111
< 0.1%
-2.10702 × 10111
< 0.1%
-2.07919 × 10111
< 0.1%
-1.99824 × 10111
< 0.1%
-1.9204 × 10111
< 0.1%
-7.441628207 × 10101
< 0.1%
-7.116831665 × 10101
< 0.1%
ValueCountFrequency (%)
7.71572 × 10111
< 0.1%
7.5599 × 10111
< 0.1%
7.3475 × 10111
< 0.1%
7.16742 × 10111
< 0.1%
1.82867 × 10111
< 0.1%
1.75821 × 10111
< 0.1%
1.74285 × 10111
< 0.1%
1.71735 × 10111
< 0.1%
8899774207 1
< 0.1%
8653201119 1
< 0.1%

Income (Loss) from Continuing Operations
Real number (ℝ)

High correlation  Skewed 

Distinct79996
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0352205 × 108
Minimum-1.35241 × 1012
Maximum2.28181 × 1012
Zeros5
Zeros (%)< 0.1%
Negative39070
Negative (%)48.8%
Memory size625.1 KiB
2025-03-23T05:26:18.848098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.35241 × 1012
5-th percentile-3.2736955 × 108
Q1-43434314
median1035094.4
Q31.4057907 × 108
95-th percentile1.8341641 × 109
Maximum2.28181 × 1012
Range3.63422 × 1012
Interquartile range (IQR)1.8401339 × 108

Descriptive statistics

Standard deviation1.8936834 × 1010
Coefficient of variation (CV)46.92887
Kurtosis10540.517
Mean4.0352205 × 108
Median Absolute Deviation (MAD)72062879
Skewness60.340539
Sum3.2281764 × 1013
Variance3.5860368 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:18.992447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
< 0.1%
-331278476.6 1
 
< 0.1%
-310957824.5 1
 
< 0.1%
-32447097.77 1
 
< 0.1%
-5835671.663 1
 
< 0.1%
-54720048.1 1
 
< 0.1%
-55714607.14 1
 
< 0.1%
-259713672.2 1
 
< 0.1%
-222364655.3 1
 
< 0.1%
-29820980.19 1
 
< 0.1%
Other values (79986) 79986
> 99.9%
ValueCountFrequency (%)
-1.35241 × 10121
< 0.1%
-1.34621 × 10121
< 0.1%
-1.3405 × 10121
< 0.1%
-1.33413 × 10121
< 0.1%
-5.27579 × 10111
< 0.1%
-5.24804 × 10111
< 0.1%
-5.18735 × 10111
< 0.1%
-5.17115 × 10111
< 0.1%
-5.427673538 × 10101
< 0.1%
-5.375762507 × 10101
< 0.1%
ValueCountFrequency (%)
2.28181 × 10121
< 0.1%
2.27004 × 10121
< 0.1%
2.13707 × 10121
< 0.1%
2.13204 × 10121
< 0.1%
1.11701 × 10111
< 0.1%
1.09288 × 10111
< 0.1%
1.09046 × 10111
< 0.1%
1.07135 × 10111
< 0.1%
1.04363 × 10111
< 0.1%
1.03259 × 10111
< 0.1%

Net Income
Real number (ℝ)

High correlation  Skewed 

Distinct79871
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9642709 × 108
Minimum-1.46545 × 1012
Maximum2.20966 × 1012
Zeros130
Zeros (%)0.2%
Negative38949
Negative (%)48.7%
Memory size625.1 KiB
2025-03-23T05:26:19.125911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.46545 × 1012
5-th percentile-3.2647607 × 108
Q1-43027542
median961837.11
Q31.3702309 × 108
95-th percentile1.8077949 × 109
Maximum2.20966 × 1012
Range3.67511 × 1012
Interquartile range (IQR)1.8005063 × 108

Descriptive statistics

Standard deviation1.9047175 × 1010
Coefficient of variation (CV)48.047108
Kurtosis9953.5425
Mean3.9642709 × 108
Median Absolute Deviation (MAD)71316773
Skewness50.78312
Sum3.1714167 × 1013
Variance3.6279489 × 1020
MonotonicityNot monotonic
2025-03-23T05:26:19.260316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
0.2%
-342593872.4 1
 
< 0.1%
-402479639.3 1
 
< 0.1%
-32908137.4 1
 
< 0.1%
-5451705.218 1
 
< 0.1%
-55235775.55 1
 
< 0.1%
-52104281.26 1
 
< 0.1%
-243988448.9 1
 
< 0.1%
-218668195.7 1
 
< 0.1%
925492142.1 1
 
< 0.1%
Other values (79861) 79861
99.8%
ValueCountFrequency (%)
-1.46545 × 10121
< 0.1%
-1.46187 × 10121
< 0.1%
-1.41755 × 10121
< 0.1%
-1.38209 × 10121
< 0.1%
-5.57714 × 10111
< 0.1%
-5.56444 × 10111
< 0.1%
-5.44048 × 10111
< 0.1%
-5.35819 × 10111
< 0.1%
-5.548356114 × 10101
< 0.1%
-5.483540547 × 10101
< 0.1%
ValueCountFrequency (%)
2.20966 × 10121
< 0.1%
2.20896 × 10121
< 0.1%
2.13558 × 10121
< 0.1%
2.09305 × 10121
< 0.1%
1.15826 × 10111
< 0.1%
1.13136 × 10111
< 0.1%
1.09278 × 10111
< 0.1%
1.0666 × 10111
< 0.1%
1.039 × 10111
< 0.1%
1.03388 × 10111
< 0.1%

Interactions

2025-03-23T05:26:06.742532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:19.471966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.806261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:24.835969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:27.366418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:29.912248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:32.265541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:35.106188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:38.234519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:40.450091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:43.219653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:45.623729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.873659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:51.539976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.918801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:56.140862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:58.482443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:01.498598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:04.312267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.852703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:19.596500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.918660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:25.241820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:27.470273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:30.025583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:32.428860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:35.210103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:38.342411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:40.575136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:43.331575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:45.729131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:48.021296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:51.658345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:54.026994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:56.250960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:58.606128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:01.661837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:04.430049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.976566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:19.707987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:22.035754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:25.393498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:27.607583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:30.150277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:32.607690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-23T05:26:05.843522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:08.310168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:20.955211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:23.767433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:26.681352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:28.942645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:31.490828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:34.107544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:37.267731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:39.772373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:42.093643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:44.826373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.154560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:50.234257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.149877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:55.429265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:57.746445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:00.043236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:03.590558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:05.972253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:08.440789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.227970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:23.944354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:26.802954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:29.054031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:31.627159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:34.225517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:37.445162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:39.891417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:42.580931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:44.961434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.261280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:50.415249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.267865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:55.545755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:57.860980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:00.153956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:03.701492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.095769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:08.573539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.344966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:24.103160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:26.917529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:29.172413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:31.752535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:34.342494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:37.644052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:40.000376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:42.709840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:45.083808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.379602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:50.600513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.395072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:55.676028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:58.002527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:00.265880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:03.819485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.220124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:08.692744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.446001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:24.265572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:27.026666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:29.521398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:31.869911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:34.444865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:37.816551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:40.108527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:42.836589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:45.211637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.487603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:50.729811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.509450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:55.777688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:58.114453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:00.372890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:03.940343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.353011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:08.815437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.579020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:24.456150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:27.138513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:29.655915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:32.016230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:34.569806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:37.979736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:40.221920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:42.963130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:45.334464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.637856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:51.283816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.659074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:55.902842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:58.234079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:01.164804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:04.059034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.477978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:08.939526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:21.697985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:24.657544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:27.251442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:29.781342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:32.140651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:34.693855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:38.108413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:40.346431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:43.090457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:45.468960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:47.755841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:51.409665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:53.783156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:56.018713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:25:58.360693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:01.337942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:04.180910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T05:26:06.620800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-23T05:26:19.387848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Cost of RevenueDepreciation & AmortizationFiscal YearGross ProfitIncome (Loss) from Continuing OperationsIncome Tax (Expense) Benefit, NetInterest Expense, NetNet IncomeNet Income (Common)Non-Operating Income (Loss)Operating ExpensesOperating Income (Loss)Pretax Income (Loss)Pretax Income (Loss), Adj.Research & DevelopmentRevenueSelling, General & AdministrativeShares (Basic)Shares (Diluted)
Cost of Revenue1.0000.6830.001-0.842-0.5260.5700.644-0.522-0.5190.4680.796-0.685-0.540-0.6130.651-0.9630.808-0.549-0.541
Depreciation & Amortization0.6831.0000.004-0.802-0.4010.3520.800-0.396-0.3900.6660.811-0.613-0.405-0.4740.544-0.7680.598-0.639-0.635
Fiscal Year0.0010.0041.000-0.005-0.002-0.0030.007-0.003-0.0030.0040.003-0.004-0.002-0.003-0.010-0.0010.001-0.001-0.001
Gross Profit-0.842-0.802-0.0051.0000.599-0.609-0.6720.5930.589-0.478-0.9300.7640.6080.686-0.7640.941-0.8760.6560.649
Income (Loss) from Continuing Operations-0.526-0.401-0.0020.5991.000-0.663-0.3520.9810.978-0.210-0.3600.8480.9870.9080.0510.567-0.3520.2870.289
Income Tax (Expense) Benefit, Net0.5700.352-0.003-0.609-0.6631.0000.340-0.654-0.6530.1910.497-0.668-0.717-0.6950.368-0.6020.505-0.357-0.355
Interest Expense, Net0.6440.8000.007-0.672-0.3520.3401.000-0.350-0.3440.8390.647-0.576-0.358-0.4350.236-0.6890.578-0.488-0.485
Net Income-0.522-0.396-0.0030.5930.981-0.654-0.3501.0000.997-0.208-0.3590.8350.9690.8920.0450.563-0.3530.2860.288
Net Income (Common)-0.519-0.390-0.0030.5890.978-0.653-0.3440.9971.000-0.204-0.3560.8310.9660.8890.0440.560-0.3500.2830.285
Non-Operating Income (Loss)0.4680.6660.004-0.478-0.2100.1910.839-0.208-0.2041.0000.464-0.428-0.213-0.2730.073-0.5050.411-0.328-0.326
Operating Expenses0.7960.8110.003-0.930-0.3600.4970.647-0.359-0.3560.4641.000-0.515-0.369-0.4340.860-0.8810.931-0.660-0.654
Operating Income (Loss)-0.685-0.613-0.0040.7640.848-0.668-0.5760.8350.831-0.428-0.5151.0000.8550.943-0.0010.735-0.4950.3990.400
Pretax Income (Loss)-0.540-0.405-0.0020.6080.987-0.717-0.3580.9690.966-0.213-0.3690.8551.0000.9180.0480.579-0.3620.2920.294
Pretax Income (Loss), Adj.-0.613-0.474-0.0030.6860.908-0.695-0.4350.8920.889-0.273-0.4340.9430.9181.0000.0220.659-0.4290.3400.341
Research & Development0.6510.544-0.010-0.7640.0510.3680.2360.0450.0440.0730.860-0.0010.0480.0221.000-0.6390.754-0.606-0.600
Revenue-0.963-0.768-0.0010.9410.567-0.602-0.6890.5630.560-0.505-0.8810.7350.5790.659-0.6391.000-0.8710.6130.606
Selling, General & Administrative0.8080.5980.001-0.876-0.3520.5050.578-0.353-0.3500.4110.931-0.495-0.362-0.4290.754-0.8711.000-0.589-0.582
Shares (Basic)-0.549-0.639-0.0010.6560.287-0.357-0.4880.2860.283-0.328-0.6600.3990.2920.340-0.6060.613-0.5891.0000.987
Shares (Diluted)-0.541-0.635-0.0010.6490.289-0.355-0.4850.2880.285-0.326-0.6540.4000.2940.341-0.6000.606-0.5820.9871.000

Missing values

2025-03-23T05:26:09.168864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T05:26:09.566914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-23T05:26:10.206387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TickerReport DateCurrencyFiscal YearFiscal PeriodPublish DateRestated DateShares (Basic)Shares (Diluted)RevenueCost of RevenueGross ProfitOperating ExpensesSelling, General & AdministrativeResearch & DevelopmentDepreciation & AmortizationOperating Income (Loss)Non-Operating Income (Loss)Interest Expense, NetPretax Income (Loss), Adj.Net Income (Common)Pretax Income (Loss)Income Tax (Expense) Benefit, NetIncome (Loss) from Continuing OperationsNet Income
0A31-10-2019USD1931.154471FY19-12-201917-12-20213.207423e+083.088419e+085.096765e+09-2.244715e+092.895128e+09-1.811634e+09-1.418599e+09-3.862866e+08NaN9.739130e+08-2.154045e+07-3.960925e+078.757926e+081.087814e+099.014980e+081.536230e+081.020157e+091.028148e+09
1A31-10-2020USD2087.780254FY18-12-202021-12-20223.049236e+083.245600e+085.267673e+09-2.436504e+092.844908e+09-2.015411e+09-1.479589e+09-5.014586e+08NaN8.599826e+08-3.843031e+06-6.942833e+078.821196e+087.250344e+088.622497e+08-1.191471e+087.016688e+087.474341e+08
2A31-10-2021USD1923.825389FY17-12-202120-12-20233.027458e+082.918267e+086.487998e+09-2.923344e+093.410192e+09-2.096944e+09-1.582244e+09-4.232309e+08NaN1.360027e+091.350341e+07-7.820687e+071.369544e+091.229461e+091.382063e+09-1.501262e+081.189369e+091.269765e+09
3A31-10-2022USD1997.047437FY21-12-202220-12-20243.029202e+082.932278e+086.812952e+09-3.217637e+093.823632e+09-2.097907e+09-1.560966e+09-4.765853e+08NaN1.629868e+09-1.180359e+08-7.680476e+071.511038e+091.264476e+091.452603e+09-2.597913e+081.295889e+091.220533e+09
4A31-10-2023USD1970.941824FY20-12-202320-12-20242.958585e+082.961714e+086.657088e+09-3.348499e+093.518646e+09-2.199863e+09-1.694759e+09-4.635090e+08NaN1.343784e+09-1.123648e+07-4.595449e+071.362767e+091.279250e+091.298291e+09-9.724238e+071.284174e+091.259117e+09
5A2131-12-2019USD1999.882746FY09-03-202019-04-20221.797466e+092.203125e+092.890160e+08-2.835967e+08-9.203109e+04-1.793123e+09-6.874372e+08-1.166327e+09NaN-1.795198e+09-1.333093e+08-4.129798e+06-1.925404e+09-3.125548e+09-2.300256e+09NaN-2.503980e+09-2.282183e+09
6A2131-12-2020USD2044.035873FY10-03-202121-04-20231.928285e+092.168048e+098.987454e+09-7.799553e+091.589027e+09-2.244015e+09-1.073444e+09-1.069229e+09NaN-6.950046e+082.112385e+08-6.731000e+07-4.683352e+08-8.132340e+08-1.947471e+082.312788e+07-1.621938e+08-1.546508e+08
7AA31-12-2019USD1956.924332FY21-02-202024-02-20221.847653e+081.866307e+081.004741e+10-8.401986e+091.830419e+09-1.063548e+09-2.804661e+08-2.665164e+07-747687444.28.792464e+08-2.935889e+08-1.207804e+085.721879e+08-1.129683e+09-4.323668e+08-4.163873e+08-8.883773e+08-1.091499e+09
8AA31-12-2020USD2114.319686FY25-02-202123-02-20231.884250e+081.867222e+089.553923e+09-7.794403e+091.295292e+09-8.960932e+08-2.091478e+08-2.588079e+07-620714062.84.494068e+08-1.592903e+08-1.393550e+082.782330e+08-1.754997e+081.707362e+08-1.886984e+08-1.454257e+07-1.672056e+08
9AA31-12-2021USD1934.391801FY24-02-202221-02-20241.911971e+081.803529e+081.166212e+10-9.214990e+092.852204e+09-9.485185e+08-2.174047e+08-3.027764e+07-689661941.32.055207e+092.410947e+08-1.980224e+082.360968e+094.249504e+081.225503e+09-6.379112e+085.602838e+084.217055e+08
TickerReport DateCurrencyFiscal YearFiscal PeriodPublish DateRestated DateShares (Basic)Shares (Diluted)RevenueCost of RevenueGross ProfitOperating ExpensesSelling, General & AdministrativeResearch & DevelopmentDepreciation & AmortizationOperating Income (Loss)Non-Operating Income (Loss)Interest Expense, NetPretax Income (Loss), Adj.Net Income (Common)Pretax Income (Loss)Income Tax (Expense) Benefit, NetIncome (Loss) from Continuing OperationsNet Income
79990LUCD31-12-2020USD1985.491614FY06-04-202106-04-20221.475639e+07NaNNaNNaNNaN-8.279380e+06-2.973746e+06-5.600886e+06NaN-7.992092e+06NaNNaN-8.522698e+06-8.660866e+06-8.020206e+06NaN-8.428849e+06-8.141793e+06
79991LUCY31-12-2021USD1988.044493FY24-03-202224-03-20232.910889e+053.377852e+056.618872e+05-5.496798e+051.515134e+05-3.338971e+06-3.076618e+06-8.452255e+04NaN-3.324571e+06-4.063439e+04-4.002386e+04-3.370365e+06-3.141103e+06-3.160704e+06NaN-3.249974e+06-3.304942e+06
79992LULU31-01-2020USD2016.388310FY26-03-202029-03-20221.316247e+081.274446e+084.018019e+09-1.834073e+092.179859e+09-1.295356e+09-1.270508e+09NaN-3.029323e+049.242305e+088.313021e+06NaN9.233540e+086.700594e+089.162643e+08-243953950.86.230378e+086.636658e+08
79993LULU31-01-2021USD2000.474902FY30-03-202128-03-20231.319337e+081.315683e+084.554460e+09-1.956187e+092.459571e+09-1.585765e+09-1.643419e+09NaN-4.973777e+068.572348e+08-6.221950e+05NaN8.754344e+085.718959e+088.284127e+08-226356606.66.182141e+085.661632e+08
79994LULU31-01-2022USD2019.964511FY29-03-202221-03-20241.259277e+081.290363e+086.476815e+09-2.612823e+093.595698e+09-2.273213e+09-2.181378e+09NaN-8.431944e+061.380237e+094.947828e+05NaN1.347482e+099.537310e+081.398383e+09-341337279.09.409159e+089.670766e+08
79995LULU31-01-2023USD2060.073629FY28-03-202321-03-20241.253191e+081.267640e+087.747207e+09-3.660570e+094.473080e+09-2.808869e+09-2.658702e+09NaN-8.962733e+061.803293e+094.108312e+06NaN1.671045e+098.620482e+081.340395e+09-485054715.98.538824e+088.549633e+08
79996LULU31-01-2024USD2075.484182FY21-03-202421-03-20241.299788e+081.272724e+089.183537e+09-3.934970e+095.571569e+09-3.232646e+09-3.518835e+09NaN-4.952309e+062.261688e+094.370649e+07NaN2.263999e+091.482153e+092.174615e+09-615277215.91.588061e+091.575942e+09
79997LUMN31-12-2019USD2018.087043FY28-02-202024-02-20221.118279e+091.026024e+092.064411e+10-8.880439e+091.180038e+10-8.723203e+09-3.542382e+09NaN-5.059373e+093.649999e+09-1.941150e+09-2.073103e+091.779046e+09-5.426273e+09-4.774960e+09-520830168.4-5.217810e+09-5.212864e+09
79998LUMN31-12-2020USD2019.335490FY25-02-202123-02-20231.078558e+091.082850e+092.123628e+10-9.114310e+091.151866e+10-8.097674e+09-3.310146e+09NaN-4.612149e+093.727934e+09-1.819487e+09-1.652119e+091.807334e+09-1.292425e+09-7.681166e+08-449269514.2-1.193273e+09-1.278844e+09
79999LUMN31-12-2021USD1923.935533FY24-02-202222-02-20241.099067e+091.113571e+092.031176e+10-8.397640e+091.119670e+10-6.881624e+09-2.979383e+09NaN-4.177787e+094.475944e+09-1.659343e+09-1.448826e+092.787815e+092.106021e+092.571730e+09-700514891.12.126533e+092.115489e+09